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Applied Sciences
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Artificial Intelligence and Robotics in Healthcare
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Artificial Intelligence and Robotics in Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 33490

Special Issue Editors

Dr. Nouman Ali

E-Mail Website
Guest Editor
Department of Software Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan
Interests: deep learning; image classification; artificial intelligence
Dr. Ihsan Rabbi

E-Mail Website
Guest Editor
Department of Computer Sciences, University of Science and Technology Bannu, Khyber Pakhtunkhwa 28100, Pakistan
Interests: artificial intelligence; virtual and augmented reality

Special Issue Information

Dear Colleagues,

The recent advancement in the field of artificial intelligence (AI) and robotics has assisted humans in different healthcare applications. Due to artificial intelligence and robotics, good results have been obtained in research and medical applications relevant to cancer detection, automation of different surgical process, genetic testing and early detection of various diseases. AI is mainly used for predictions that can assist humans in different health care applications such as automatic detection of diseases, accurate diagnosis, decision support systems that can assist patients and medical practitioners, applications based on Internet of Medical Things, E-Health management, early disease detection and clinical decision support systems. The advancement in robotics is assisting humans through remote treatment, auxiliary robots and accurate diagnostics using robotics. The aim of this Special Issue is to peer review and publish the original research and review articles that are relevant to AI and robotics in healthcare. This Special Issue will explore a variety of interconnected topics, including but not limited to:

  • Applications of machine learning in health care.
  • Disease detection and diagnosis.
  • Applications of deep learning in health care.
  • Remote treatment.
  • Performance of tasks that can assist health care systems.
  • Decision support systems that can assisting patients and medical practitioners.
  • Application of AI with the Internet of Medical Things.
  • E-Health data management using AI.
  • Early detection of dieses through intelligent systems.
  • AI-based support for clinical decision making.
  • Auxiliary robots.
  • Application of robotics in healthcare systems.
  • Medical image classification.
  • Accurate diagnosis using robotics and AI.

Dr. Nouman Ali
Dr. Ihsan Rabbi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in healthcare
  • robotics in healthcare, smart healthcare systems
  • disease detection using AI, decision support systems for healthcare

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Published Papers (6 papers)

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Research

18 pages, 14493 KiB  
Article
The Development of a Secure Internet Protocol (IP) Network Based on Asterisk Private Branch Exchange (PBX)
by Mubarak Yakubova, Olga Manankova, Assel Mukasheva, Alimzhan Baikenov and Tansaule Serikov
Appl. Sci. 2023, 13(19), 10712; https://doi.org/10.3390/app131910712 - 26 Sep 2023
Cited by 1 | Viewed by 1513
Abstract
The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern [...] Read more.
The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
►▼ Show Figures

Figure 1

Figure 1
<p>The developed network based on Asterisk IP PBX.</p> Full article ">Figure 2
<p>The TLS Handshake Protocol.</p> Full article ">Figure 3
<p>Checking packages in the repository.</p> Full article ">Figure 4
<p>Running Asterisk status check.</p> Full article ">Figure 5
<p>Checking the installed version.</p> Full article ">Figure 6
<p>Copying default settings.</p> Full article ">Figure 7
<p>Opening ports for Asterisk.</p> Full article ">Figure 8
<p>SIP settings on remote server No. 1.</p> Full article ">Figure 9
<p>Dialplan settings on remote server No. 2.</p> Full article ">Figure 10
<p>Output of connected clients.</p> Full article ">Figure 11
<p>Registering a remote server No. 2 on No. 1.</p> Full article ">Figure 12
<p>Captured SIP packet data.</p> Full article ">Figure 13
<p>RTP packet data.</p> Full article ">Figure 14
<p>RTP stream analysis.</p> Full article ">Figure 15
<p>Total load.</p> Full article ">Figure 16
<p>Generating the certificate and keys of the Asterisk server.</p> Full article ">Figure 17
<p>Software for generating client certificates and keys.</p> Full article ">Figure 18
<p>Certificate information software.</p> Full article ">Figure 19
<p>Transfer of encrypted session information.</p> Full article ">Figure 20
<p>Transfer of encrypted media traffic.</p> Full article ">
23 pages, 5456 KiB  
Article
Orientation Control Design of a Telepresence Robot: An Experimental Verification in Healthcare System
by Ali Altalbe, Muhammad Nasir Khan, Muhammad Tahir and Aamir Shahzad
Appl. Sci. 2023, 13(11), 6827; https://doi.org/10.3390/app13116827 - 4 Jun 2023
Cited by 2 | Viewed by 2190
Abstract
Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are [...] Read more.
Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are many challenges in obtaining human–robot interactions regarding maneuverability, controllability, stability, drive layout and autonomy. In this paper, we proposed a stability and control design for a telepresence robot called auto-MERLIN. The proposed design simulated and experimentally verified self-localization and maneuverability in a hazardous environment. A model from Rieckert and Schunck was initially considered to design the control system parameters. The system identification approach was then used to derive the mathematical relationship between the manipulated variable of robot orientation control. The theoretical model of the robot mechanics and associated control were developed. A design model was successfully implemented, analyzed mathematically, used to build the hardware and tested experimentally. Each level takes on excellent tasks for the development of auto-MERLIN. A higher level always uses the services of lower levels to carry out its functions. The proposed approach is comparatively simple, less expensive and easily deployable compared to previous methods. The experimental results showed that the robot is functionally complete in all aspects. A test drive was performed over a given path to evaluate the hardware, and the results were presented. Simulation and experimental results showed that the target path is maintained quite well. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
►▼ Show Figures

Figure 1

Figure 1
<p>Attached robot on the Monster Truck HPI Savage 2.1.</p> Full article ">Figure 2
<p>Robot orientation controller.</p> Full article ">Figure 3
<p>Simplified single-track model from Rieckert and Schunck.</p> Full article ">Figure 4
<p>Orientation controller step response over a steering value step from 0 to 75.</p> Full article ">Figure 5
<p>Linearized variant of the orientation controller.</p> Full article ">Figure 6
<p>Robot trajectory and orientation for straight route driving.</p> Full article ">Figure 7
<p>Driven trajectory over the straight path.</p> Full article ">Figure 8
<p>Robot trajectory and orientation for turning right direction.</p> Full article ">Figure 9
<p>Driven trajectory over the right direction.</p> Full article ">Figure 10
<p>Self-estimates of robot position, red (clockwise) and blue (counter-clockwise).</p> Full article ">Figure 11
<p>Calculated and driven trajectories.</p> Full article ">Figure 12
<p>Experimental verification: from doctor’s office to the patient room in a healthcare system.</p> Full article ">Figure 13
<p>Different environment simulation using Gazebo software.</p> Full article ">
14 pages, 455 KiB  
Article
A Hybrid CNN-LSTM Random Forest Model for Dysgraphia Classification from Hand-Written Characters with Uniform/Normal Distribution
by Fahad Masood, Wajid Ullah Khan, Khalil Ullah, Ahmad Khan, Fatemah H. Alghamedy and Hanan Aljuaid
Appl. Sci. 2023, 13(7), 4275; https://doi.org/10.3390/app13074275 - 28 Mar 2023
Cited by 7 | Viewed by 2690
Abstract
Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated [...] Read more.
Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
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Figure 1

Figure 1
<p>Handwriting samples of a normal and a dysgraphia patient. (<b>a</b>) Normal handwriting. (<b>b</b>) Dysgraphia patient’s handwriting.</p> Full article ">Figure 2
<p>Architecture of the proposed model.</p> Full article ">Figure 3
<p>Comparison results of accuracy, specificity and sensitivity for various tasks.</p> Full article ">Figure 4
<p>Comparison results of accuracy, specificity and sensitivity for various features.</p> Full article ">Figure 5
<p>Comparison results of accuracy, specificity and sensitivity for various methods.</p> Full article ">Figure 6
<p>Comparison results of accuracy for various tasks in terms of uniform and normal distributions.</p> Full article ">Figure 7
<p>Comparison results of accuracy for various features in terms of uniform and normal distributions.</p> Full article ">
26 pages, 2735 KiB  
Article
Modeling of Multi-Level Planning of Shifting Bottleneck Resources Integrated with Downstream Wards in a Hospital
by Aisha Tayyab, Saif Ullah, Toqeer Mahmood, Yazeed Yasin Ghadi, Bushra Latif and Hanan Aljuaid
Appl. Sci. 2023, 13(6), 3616; https://doi.org/10.3390/app13063616 - 12 Mar 2023
Cited by 1 | Viewed by 1577
Abstract
Planning and scheduling critical resources in hospitals is significant for better service and profit generation. The current research investigates an integrated planning and scheduling problem at different levels of operating rooms, intensive care units, and wards. The theory of constraints is applied to [...] Read more.
Planning and scheduling critical resources in hospitals is significant for better service and profit generation. The current research investigates an integrated planning and scheduling problem at different levels of operating rooms, intensive care units, and wards. The theory of constraints is applied to make plans and schedules for operating rooms based on the capacity constraints of the operating room itself and downstream wards. A mixed integer linear programming model is developed considering shifting bottleneck resources among the operating room, intensive care unit, and hospital wards to maximize the utilization of resources at all levels of planning. Different sizes of planning and scheduling problems of the hospital, including small, medium, and large sizes, are created with variable arrivals and surgery durations and solved using a CPLEX solver for validating the developed models. Later, the application of the proposed models in the real world to develop planning systems for hospitals is discussed, and future extensions are suggested. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
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Figure 1

Figure 1
<p>Patient flow considered in this paper.</p> Full article ">Figure 2
<p>Multi-level planning considered in the current paper.</p> Full article ">Figure 3
<p>Multi-level integrated planning when the operating room is the bottleneck.</p> Full article ">Figure 4
<p>Multi-level integrated planning when the ICU is the bottleneck.</p> Full article ">Figure 5
<p>Multi-level integrated planning when the ward is the bottleneck.</p> Full article ">Figure 6
<p>CPU time for each category of problem: (<b>a</b>) operating room is the bottleneck, (<b>b</b>) ICU is the bottleneck, and (<b>c</b>) ward is the bottleneck.</p> Full article ">
19 pages, 2027 KiB  
Article
DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images
by Asaf Raza, Naeem Ullah, Javed Ali Khan, Muhammad Assam, Antonella Guzzo and Hanan Aljuaid
Appl. Sci. 2023, 13(4), 2082; https://doi.org/10.3390/app13042082 - 6 Feb 2023
Cited by 39 | Viewed by 13322
Abstract
Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate [...] Read more.
Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
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<p><b>BC</b> ultrasound images from dataset.</p> Full article ">Figure 2
<p>The abstract perspective of the proposed DeepBreasCancertNet technique.</p> Full article ">Figure 3
<p>TL of Pretrain models.</p> Full article ">Figure 4
<p>Confusion matrix of DeepBreastCancerNet.</p> Full article ">Figure 5
<p>Accuracy and loss function graph of DeepBreastCancerNet.</p> Full article ">
18 pages, 377 KiB  
Article
AI-Enabled Wearable Medical Internet of Things in Healthcare System: A Survey
by Fazli Subhan, Alina Mirza, Mazliham Bin Mohd Su’ud, Muhammad Mansoor Alam, Shibli Nisar, Usman Habib and Muhammad Zubair Iqbal
Appl. Sci. 2023, 13(3), 1394; https://doi.org/10.3390/app13031394 - 20 Jan 2023
Cited by 39 | Viewed by 11080
Abstract
Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered [...] Read more.
Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)
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<p>Applications of Internet of Medical Things (IoMT).</p> Full article ">Figure 2
<p>Categorization of wearable devices.</p> Full article ">
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